Distributed Data Acquisition Devices for Selective Audio Processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and aggregating audio and multimedia data from a large population of personal wireless devices due to computational limitations, privacy concerns, and the need for centralized data transmission, which can lead to inefficiencies and loss of information fusion benefits.
Innovation Solution
A distributed system of personal wireless devices with local processing capabilities, selective data transmission, and adaptive device configuration based on triggering events, allowing for local data analysis, selective buffering, and device enabling to determine environmental characteristics, while ensuring privacy and optimizing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio and multimedia data are transmitted from all devices to a central system for processing, then comprehensive environmental analysis can be achieved, but network bandwidth consumption increases and information fusion benefits are lost
Solution Approach 1:
The patent segments the centralized processing architecture into distributed local processing units at each device. Each device performs local audio scene analysis and environmental characteristic determination independently, then transmits only essential results or selective data to the network, rather than transmitting all raw audio data centrally. This segmentation reduces network bandwidth consumption while maintaining comprehensive environmental analysis capability through distributed computation.
Solution Approach 2:
The patent extracts and processes only the essential environmental characteristics and audio scene information locally at each device, rather than transmitting all raw multimedia data. The local processing units extract relevant features (such as audio scene type, environmental characteristics) and transmit only these extracted insights, eliminating the need to transmit redundant raw data and reducing network bandwidth consumption significantly.
2Loss of information
If all devices continuously transmit data to the network, then complete information aggregation is achieved, but power consumption increases
Solution Approach 1:
The patent implements periodic action through event-triggered data transmission and processing. Instead of continuous data transmission, devices monitor for specific triggering events (such as detected audio scenes, environmental changes, or significant events) and only activate data transmission and processing when such events occur. This periodic, event-driven approach maintains information aggregation completeness by capturing all significant events while dramatically reducing power consumption by keeping devices in low-power states between events.
Solution Approach 2:
The patent applies preliminary action by performing local processing and filtering of audio and multimedia data before transmission. Each device pre-processes incoming data locally to identify and extract only the most relevant information, preparing it for potential transmission. This preliminary local processing ensures that when data is transmitted, it contains complete and accurate information about significant events, while avoiding the energy waste of transmitting all raw data continuously.
3Device complexity
If computational tasks are centralized in one system, then processing coordination is simplified, but computational limitations prevent efficient handling of large data volumes
Solution Approach 1:
The patent segments the computational workload by distributing processing tasks across multiple independent local processing units at different devices. Each device's processing unit handles local audio scene analysis and environmental characteristic determination independently. This segmentation of computational tasks eliminates the bottleneck of centralized processing, allowing the system to efficiently handle large volumes of data from multiple devices simultaneously while maintaining manageable coordination complexity through standardized communication protocols.
Solution Approach 2:
The patent transitions from a single-dimensional centralized processing model to a multi-dimensional distributed processing architecture. Computational tasks are distributed across multiple spatial dimensions (different devices) and functional dimensions (local processing, network communication, centralized coordination). This dimensional expansion allows the system to process large data volumes efficiently by utilizing the computational resources of the entire device population rather than being constrained by a single central processor's capacity.
Data Source
AI summary
Distributed wearable and non-wearable devices, controllers and methods for processing information from a plurality of devices are provided. A distributed system includes a plurality of devices distributed in an environment. Each device has at least a communication capability for interchanging information with others of the devices and/or with a communication system. Each of at least some of the devices has one or more sensors for acquiring sensor data related to the environment proximate to the device. At least one of the communication system or one or more of the devices is configured as a controller configured to: select a subset of devices from among the plurality of devices, receive information based on the acquired sensor data of the selected subset, and combine the received information from the selected subset to determine a characteristic of the environment proximate to one or more of the devices.